Yet across sectors, many organizations are reaching the same conclusion. Adoption is proving harder than invention, especially for general-purpose technologies, as it once was for electricity. As a result, AI adoption remains on the sidelines. Systems exist, but changing how work actually gets done is far more difficult. The constraint is no longer technological capability. It is whether institutions and organizations are prepared to absorb AI. Institutions and organizations play distinct roles. Institutions are the rules, incentives, standards, and accountability structures that reduce uncertainty and make new behavior safe and trusted. Organizations operate within those rules and change workflows accordingly.

Germany pioneered the chemical industry, but the United States diffused it by embedding chemistry into manufacturing and everyday commerce. Productivity followed only after institutions evolved and organizations redesigned workflows. The United States also created the discipline of chemical engineering and applied it across sectors such as food and automobiles, drawing on its long-standing ability to attract the best and brightest talent globally and turn invention into industrial scale.

This is not a new pattern. Research on technological change shows that economic advantage rarely comes from being first to invent. It comes from the ability to diffuse new technologies broadly and productively. As Jeffrey Ding argues in Technology and the Rise of Great Powers, leadership is shaped less by breakthrough innovation and more by the capacity to absorb and deploy technology at scale.

As Nobel Prize winner Douglass North observed, institutions are the rules and incentives that shape behavior, while organizations are the actors that operate within them. That distinction explains why diffusion depends on institutional change as well as organizational capability.

Two years ago, Nandan Nilekani, co-founder of Infosys and the founding Chairman of the Unique Identification Authority of India, said that India will be the use-case capital for AI. As the architect of Aadhaar, the world’s largest biometric identity system, he said AI will change India, and India will change AI. Drawing on his experience building Aadhaar, Nilekani has argued that AI needs India as much as India needs AI because India’s scale, institutions, and use cases will shape how AI is deployed in the real economy.

Aadhaar shows what diffusion at scale actually looks like. India has more than 1.4 billion Aadhaar holders. Identities have been authenticated digitally over 164 billion times, leading to an estimated half-trillion dollars in savings by reducing leakage, duplication, and friction across the economy. UPI, built on top of these digital rails, is now the world’s largest real-time payment system, processing roughly 20 billion transactions per month.

Biometric technology was not new when Aadhaar was introduced. What changed was absorption at two levels. Institutionally, the Unique Identification Authority of India set standards, verification rules, and accountability, absorbing trust and risk at the system level.